One common example of a map used for navigation or other purposes provides a birds eye view of a plurality of buildings, roadways and other features. In such maps, the buildings are generally represented by two-dimensional (2D) footprints, at least some of which have a size and shape that is proportional to the size and shape of the respective buildings. In an effort to generate maps having depth and, therefore, and at least a three-dimensional (3D) feel, some maps of this type include shadows of the various objects including the buildings.
In order to generate maps having shadows, relatively high resolution imagery, such as imagery obtained from light detection and ranging (LIDAR) techniques, is utilized. While high resolution imagery provides data from which a map having 2D footprints of various objects including buildings and associated shadows can be generated, the capture of the high resolution imagery requires relatively sophisticated equipment and the processing of the high resolution imagery to extract the data representative of the buildings and the shadows may be computationally intensive, thereby consuming substantial processing resources.
Lower resolution imagery, such as raster imagery, is available and data representative of the buildings and the shadows may be extracted from the raster imagery without requiring utilization of as many processing resources. However, the data representative of the shadows obtained from the raster imagery includes a substantial amount of noise and speckle which results in the generation of shadows that are inaccurate or otherwise distracting. Even after the additional processing of the data representative of the shadows, such as by utilizing a convolutional neural network (CNN), deep learning techniques and/or artificial intelligence techniques, the resulting shadows in the map that is generated remain noisy and speckled and are generally of lower quality than is desired.